Ad-Hoc Information Retrieval
28 papers with code • 1 benchmarks • 2 datasets
Ad-hoc information retrieval refers to the task of returning information resources related to a user query formulated in natural language.
Benchmarks
These leaderboards are used to track progress in Ad-Hoc Information Retrieval
| Trend | Dataset | Best Model | Paper | Code | Compare |
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Robust04
SciRepEval
Latest papers with no code
Evaluating Generative Ad Hoc Information Retrieval
Recent advances in large language models have enabled the development of viable generative information retrieval systems.
A data-driven strategy to combine word embeddings in information retrieval
We use Idf combinations of embeddings to represent queries, showing that these representations outperform the average word embeddings recently proposed in the literature.
Neural document expansion for ad-hoc information retrieval
Recently, Nogueira et al. [2019] proposed a new approach to document expansion based on a neural Seq2Seq model, showing significant improvement on short text retrieval task.
A White Box Analysis of ColBERT
Transformer-based models are nowadays state-of-the-art in ad-hoc Information Retrieval, but their behavior is far from being understood.
Multi-Stage Conversational Passage Retrieval: An Approach to Fusing Term Importance Estimation and Neural Query Rewriting
Conversational search plays a vital role in conversational information seeking.
Investigating Retrieval Method Selection with Axiomatic Features
We consider algorithm selection in the context of ad-hoc information retrieval.
Fidelity-Weighted Learning
To this end, we propose "fidelity-weighted learning" (FWL), a semi-supervised student-teacher approach for training deep neural networks using weakly-labeled data.
DE-PACRR: Exploring Layers Inside the PACRR Model
Recent neural IR models have demonstrated deep learning's utility in ad-hoc information retrieval.
Toward a Deep Neural Approach for Knowledge-Based IR
With this in mind, we argue that embedding KBs within deep neural architectures supporting documentquery matching would give rise to fine-grained latent representations of both words and their semantic relations.
